Analog–digital hybrid computing with SnS2 memtransistor for low-powered sensor fusion

Algorithms for intelligent drone flights based on sensor fusion are usually implemented using conventional digital computing platforms. However, alternative energy-efficient computing platforms are required for robust flight control in a variety of environments to reduce the burden on both the battery and computing power. In this study, we demonstrated an analog–digital hybrid computing platform based on SnS2 memtransistors for low-power sensor fusion in drones. The analog Kalman filter circuit with memtransistors facilitates noise removal to accurately estimate the rotation of the drone by combining sensing data from the gyroscope and accelerometer. We experimentally verified that the power consumption of our hybrid computing-based Kalman filter is only 1/4th of that of the traditional software-based Kalman filter.


Revised INTRODUCTION (last paragraph):
In this study, we demonstrate a memtransistor (memristor with transistor structure 22 )-based analog-digital hybrid computing platform for sensor fusion with higher energy efficiency. The measured data from both the gyroscope and accelerometer were combined to accurately determine the Euler angles of drones, wherein the Kalman filter algorithm was implemented using a customized analog circuit with the memtransistor. Because this analog Kalman filter circuit can operate independently without using the computing resources of the microcontroller, the computational burden on the microcontroller is reduced, and subsequently a reduction in overall power consumption can be expected. Here, we exploited transition-metal dichalcogenide (TMD) materials to implement the memtransistor, where the bulk traps located at the tin disulfide (SnS2)-aluminum oxide (Al2O3) stack provide a highly reliable nonvolatile resistive switching behavior. The precise tunability of the SnS2 memtransistor allows for the reconfigurability of our analog-digital hybrid computing platform. Finally, we experimentally demonstrated that a drone using our hybrid computing performs sensor fusion with higher energy efficiency than a drone with only a conventional digital processor.
2) What is the advantage of using a memtransistor over a memristor? What is exactly the advantage in this application and in real-time robot control?
Response: Thank you for your queries. Because the memtransistor can control the device conductance by using the gate electrode, the peripheral circuit configuration for the memtransistor can be simplified more compared to that for the memristor. A memristor has only two electrodes, thereby Vwrite (i.e., voltages for adjusting the conductance) must be

3-terminal memtransistor
V write V write a c b d applied to both ends of the two electrodes (Fig. R1a). However, these electrodes are also used for signal input/output. Consequently, additional peripheral circuits are required to distribute the input signal (Emea(t)) and Vwrite properly (Fig. R1b).
In contrast, a memtransistor has three electrodes, and the conductance can be adjusted by using the gate electrode independently (Fig. R1c). Therefore, the conductance can be adjusted without additional peripheral circuits (Fig. R1d). In summary, there is no difference between the memristor and memtransistor in the conductance change characteristic itself, but the memtransistor enables simpler circuit configuration.
We have revised CONCLUSION section to clarify the difference between a memristor and memtransistor.

Revised CONCLUSION (last paragraph):
We believe that the power consumption of our hybrid computing platform can be further reduced when the sensor module is integrated directly into the analog Kalman filter circuit. Because the measured sensing signal is analog, the analog Kalman filter circuit can process the data directly without using any analog-to-digital conversion, thereby minimizing both the latency and quantization error. In addition, because the memtransistor has one more electrode (gate electrode) than a conventional twoterminal memristor, the device conductance can be adjusted through the gate electrode, which enables simpler circuit configuration. A simple circuit configuration is also expected to contribute in reducing the overall power consumption.
3) It is not clear how the value of the device conductance (the Kalman gain) was set. What defined it and allowed you to keep it at a constant value. Please clarify further this process.
Response: Thank you for your comment. As shown in Fig. S12, the performance of the analog Kalman filter depends on the Kalman gain value. The Kalman gain (K), where the output of analog Kalman filter (i.e., angle signal) could be ±3 V, was determined using a binary search method (trial and error). When K is 0.05 (Fig. S12), the angle signal has an amplitude of exactly ±3 V, which means that the analog Kalman filter accurately estimates the actual range of Euler angle oscillation (±30 o ). Thereafter, as discussed in Supporting Note 5-2, K is determined by K = GK/Cf, where Cf was 10 μF in our study. Consequently, the optimized device conductance (G) was 0.5 μS.
Next, as discussed in Supporting Note 4-3, the device conductance was adjusted by using the update-verify feedback method. This method is a well-known technique for accurately adjusting the conductance of a memristor [F. Alibart et al., Nanotechnology, 23, 075201, 2012]. We also demonstrated the system for this method in our previous work [S. Kim et al., Nanoscale, 11, 21449-21457, 2019]. Fig. R2 shows the flowchart for the update-verify feedback method. A detailed description of the feedback process had been added in the Supporting Note 4-3 as follows: Revised Supporting Note 4-3: Fig. S7 shows a flow chart for the update-verify process for the precise control of device conductance (G). The following process is repeated until the desired G is obtained.
(1) At the outset, Gtarget and a threshold value must be defined. Gtarget is the desired G value. The threshold value is the acceptable limit of the relative error between G and Gtarget. In our experiment, we set the threshold value as ±5%.
(3) If Gvar is within the predefined threshold (i.e., ±5 %), the feedback process is considered complete and the process is stopped. In the experimental demonstration ( Fig. 2e), approximately 10 repeated feedback processes are required to achieve the desired G value.

4)
Provide please more details about the actual effect of the noise in the control of the robot. Provide quantitative Information. Please also quantify the levels of the noise for the different sensor signals, the SNR and the effect on the robot operation.
Response: Thank you for your comment. You have requested additional explanation about the effect of noise on "the control of the robot," but our study is focused only on "the sensor fusion in drones." Therefore, we would like to focus our answer only on the effect of the sensor's noise in the operation of drones.
First, the quantitative noise performance of the sensors used in our study is as follows. In our experiment, a commercial IMU sensor (MPU6050, Supporting Note 2-1) was used because it is commonly employed in small drones. From the datasheet of MPU6050, the power spectral density of the accelerometer noise is 400 μg/√Hz (at 10 Hz). Additionally, the power spectral density of the gyroscope noise is 0.005 o /s•√Hz (at 10 Hz). We have added this information to Supporting Note 2-1.
Furthermore, we have already discussed the effect of sensor noise in Supporting Note 2-2.  overestimate the drone's actual oscillation range (±30°). Therefore, because of the intrinsic noise from the gyroscope and accelerometer, the Euler angles cannot be estimated accurately without the sensor fusion.
We have added these explanations to Supporting Note 2-2 as follows: Revised Supporting Note 2-2: Fig. S2a and Fig. S2b show the measured raw data of angular velocities (p, q, and r) and accelerations (Ax, Ay, and Az), respectively. Note that the measured angular velocities do not contain any noticeable noise but the measured accelerations contain a high frequency noise. The effect of a sensor's the estimated roll and pitch angles are sufficiently consistent with the drone's actual oscillation (the amplitude of ±30°). However, a drift in which the error gradually accumulates over time is observed. Conversely, when the data of accelerations are only used (Fig. S2d), there is no drift, but the estimated roll and pitch angles include a high frequency noise. In addition, the oscillation range is also overestimated. Therefore, due to an intrinsic bias instability from the gyroscope and a high frequency noise from the accelerometer, Euler angles cannot be estimated accurately without exploiting the sensor fusion. The ultimate goal of the neuromorphic system research is to implement an energy-efficient computing system that can overcome the limitations of the conventional digital processor by mimicking the advantages/features of biological neural networks (i.e., analog and massive parallelism). However, because the operating principles of neural networks (mechanisms of learning and cognitive processes) have not yet been clearly understood, it is currently impossible to demonstrate a neuromorphic system based on an artificial neural network that can replace all functions of conventional digital processors. Therefore, the research on neuromorphic systems currently in progress has been mainly focused on alleviating the burden of complex calculations (especially in machine learning) by using crossbar arrays of memristors . A key machine learning algorithm heavily relies on matrix vector multiplications (MVM), wherein high-density crossbar array of memristors is well-suited to accelerate such algebraic operations.

5) Why not all the computation or
The analog-digital hybrid circuit studied in our work is also a branch of the neuromorphic system research.
The analog switching behavior of the memristor is useful for energy-efficient analog computing and enables functional reconfigurability of the circuit. There are several proposals and experimental demonstrations of simple circuits exploiting the analog properties of memristors, e.g., tunable gain in operating amplifiers [R. Our research does not aim to implement a computing system that can process all data analogously like the human brain. On the contrary, our goal is to realize an analog-digital hybrid computing system in which analog circuits can reduce the overall power consumption while maintaining the performance of the digital processor (i.e., high arithmetic accuracy and speed). In our next study, we will try to experimentally demonstrate more complex computations than sensor fusion by implementing an advanced analog-digital hybrid circuit.
We revised INTRODUCTION to clarify the purpose of the analog-digital hybrid computing platform as follows:

Revised INTRODUCTION (4 th paragraph):
Notably, an analog-digital hybrid computing platform, which is inspired by biological neural networks (typically referred to as neuromorphic systems 8 ), has been considered as a promising candidate for realizing energy-efficient computing. 9-13 The precisely tunable analog resistive switch (i.e., memristor) enables energy-efficient analog computation with a process-in-memory architecture and also allows for functional reconfigurability. The feasibility of the analog-digital hybrid computing platform has been successfully demonstrated to mitigate the computational burden of vector-matrix multiplication in the calculation of various machine-learning algorithms. 14-16 The research to reduce overall energy consumption by replacing a part of digital calculation with analog circuits is being conducted in various application fields.
Furthermore, recent advancements in memristors based on two-dimensional materials offer the possibility of designing new materials with atomic-level precision, resulting in excellent resistive switching performance with only a small amount of energy consumption. 17-20 A drone is a complex real-time sensing system that can benefit substantially from this memristor-based analog-digital hybrid computing platform. Because the Kalman filter algorithm can be expressed by linear equations, it can be implemented using memristor-based analog circuits.
Moreover, this memristor-based analog component can operate independently without using computing resources from the digital processor, thereby reducing the computational load of the digital component.
Nevertheless, there exist some demonstrations of memristor-based hybrid computing for drones but only one recent study has applied this to control an inverted pendulum for a mobile robot. 21 6) Please provide more information on how the conductance is being measured, what signals, values, apparatus, setup, etc.
Response: Thank you for your comment. As shown in Fig. R3, the electrical pulses (VG and VD) were generated by a function generator (Keysight 33622a) and drain current (ID) was measured by a source-measurement unit (SMU, Keysight B2902a). The pulse was applied to the gate and drain electrodes, and the drain current was measured through the source electrode. Figure R3. Setup for the characterization of our memtransistor's electrical performance.
The device conductance was measured through a read pulse (VG = 0 V, VD = 0.5 V, 50 ms), as shown in Fig. 2d.
The device conductance (G) was then calculated by G = ID/VD. We confirmed that the read pulse is nondestructive; hence, the device conductance cannot be changed by the read pulse.
We add this information to the METHODS section.

Added to METHODS:
The electrical pulses (VG and VD) were generated by a function generator (Keysight 33622a) and drain current (ID) was measured by a source-measurement unit (SMU, Keysight B2902a). The pulse was applied to the gate and drain electrodes, and the drain current was measured through the source electrode. Additionally, to evaluate the power consumption in real-time, a current waveform analyzer (Keysight, CX3300) was used to monitor the amount of current flow to both the digital and analog components. Thank you for your question. We used an operational amplifier (op-amp, Linear Technology, LT6005) for our analog Kalman filter because it is specially designed for low-power applications (Fig. R4). This op-amp can operate on power supplies as low as 1.6 V while only drawing a maximum of 1 µA quiescent current.

8) Please explain why a memtransistor was not use for the second signal.
Response: Thank you for your comment. As pointed out by the reviewer, two memtransistors are actually required for each input signal ( () mea Et and () mea t  ) to fuse the signals according to the Kalman gain.

V
However, in our experiment, one memtransistor was intentionally utilized for an easier understanding of the sensor fusion algorithm (i.e., Kalman filtering used in this study).
In the Kalman filtering process, there is only one variable that needs to be continuously updated, i.e., the Kalman gain. Therefore, we corresponded one Kalman gain value to one memtransistor conductance. Although GK, Rw, and Cf contribute to determining the Kalman gain value in the analog Kalman filter circuit, we used a single memtransistor to effectively convey our idea that the Kalman gain can be stored in a non-volatile memtransistor.

9) How is the transfer of the SnS2 layer is performed, what is the throughput and repeatability of the process? How well defined are the device parameters using this process?
Response: Thank you for your queries. First, we added the following explanations about the transfer method of the SnS2 layer to the METHODs section: Revised METHODS section: The SnS2 memtransistor, which served as the gate electrode, was fabricated on a heavily n-doped Si wafer with a resistivity of less than 0.005 Ω•cm (QL Electronics Co.). A 20-nm-thick aluminum oxide (Al2O3) film, which is a gate dielectric layer, was grown via atomic layer deposition (Nano-ALD2000, IPS) at 350 °C. In the next step, we obtained a thin flake of SnS2 from bulk SnS2 (HQ Graphene) by mechanical exfoliation using the scotch tape method and transferred it gently on top of a polydimethylsiloxane (PDMS) stamp. We selected the desired flake on PDMS through an optical microscope and transferred it onto a Si/Al2O3 substrate (dry transfer method) through a micromanipulator. Finally, the source and drain electrodes were patterned using electron beam lithography. Ti/Au (10 nm/50 nm) metals were deposited using a thermal evaporator at high vacuum pressure (~10 6 Torr) and patterned using a conventional lift-off process.
Next, Fig. R5 shows the measured ID-VG curves from 6 devices fabricated using the same process. There is a slight device-to-device variation. Because the transfer method of the SnS2 layer is a manual process, we believe that that the variation is owing to the thickness variation of the SnS2 layer for each device. Nevertheless, this variation is not an issue in the operation of our analog-digital hybrid computing platform. Regardless of the device variation, the desired channel conductance can be precisely adjusted through the update-verify feedback method discussed in Supporting Note 4-3. Figure R5. Measured ID-VG curves from 6 different devices.

10) Why haven't you made a proper PCB and a breadboard is being used?
Response: Thank you for your question. In this work, for the proof-of-the concept of our analog-digital computing platform, we implemented the analog circuit on a breadboard.
In fact, to integrate our analog Kalman filter circuit on a PCB and mount it on the drone, 1) a circuit for generating a voltage pulse, 2) a circuit for performing the update-verify feedback process, and 3) a circuit for power distribution are additionally required. Therefore, further research on the design of these peripheral circuits is necessary. We hope that the system-level analog-digital hybrid computing platform will be demonstrated in a near-future study.

11) In my opinion the main paper should show some results from the operation and control of the robot
with and without the memtransistors and show some results demonstrating the usefulness of the proposed approach, rather than having all results in the SI.
Response: Thank you for your comment. We have shown the comparative results with and without a memtransistor in Fig. 4d. Fig. 4d shows the output of our memtransistor-based analog Kalman filter (red curve).
Note that the black dotted curve denotes the output of the traditional Kalman filter, which is a purely softwarecalculated result obtained by the microcontroller without using any memtransistor. It is obvious that both results show a good consistency, which guarantees the feasibility of our analog Kalman filter circuit.
We have added further explanations to section 3.1 clarify the comparative results with/without memtransistor as follows: Revised Section 3.2 (pages 11-12): Moreover, the output of our memtransistor-based analog Kalman filter shows good agreement with the result of the traditional software-based Kalman filter (black dotted curve in Fig.   4d), which was calculated entirely on the microcontroller without using the memtransistor. The consistency of these results with/without the memtransistor shown in Fig. 4d guarantees the feasibility of our analog Kalman filter circuit.

12) With regards to ref 21, the main differences are that you used a memtransistor as opposed to a
memristor and you only considered the power consumption, when these other authors also considered the computational speed and its effect on robot control. Both used Kalman filtering and fusion.

Consequently, for at this point the innovation does not appear to be significant. Please highlight further
what your innovation is, especially when compared to that other paper.
Response: Thank you for your comment. he differences between our study and Ref.
[21] utilized a two-terminal memristor, but our work utilized a three-terminal memtransistor. As already answered in Question#2, the memtransistor enables a simpler circuit configuration.
[21] used a Pt/Al2O3/Ta/Pt memristor device and this metal-oxide based memristor has already been studied in many previous works. In contrast, we demonstrated a reliable resistive switching characteristic using a new material (SnS2 nanosheet) and analyzed its physical mechanism.
3) The performance of resistive switching obtained from our SnS2 memtransistor is better than that of the memristor in Ref. [21]. The endurance of our SnS2 memtransistor is above 10 5 (Fig. 2d), but that of the memristor in Ref.
[21] is approximately 5,000. Additionally, the operation current level of our SnS2 memtransistor during the resistive switching is under 1 μA (Fig. 2d), but that of the memristor in Ref.
< Comparison of the sensor fusion > 1) As answered in Question#11, in our study, the output of our memtransistor-based analog Kalman filter is comparatively analyzed with the result from a traditional software-calculated Kalman filter. This comparative study clearly guarantees the feasibility of our analog Kalman filter circuit. However, Ref.
[21] did not provide any comparative analysis with/without analog-digital hybrid computing in the sensor fusion process.
[21] mainly focused on the improvement of "speed" (response time) in robot control, where PD controller operation was accelerated through analog-digital hybrid computing. In contrast, our study mainly focused on the improving the accuracy and energy-efficiency in sensor fusion through analogdigital hybrid computing. Therefore, the goals of Ref.
[21] and our study are completely different.
We revised the CONCLUSIONS section to clarify the originality of our work more clearly. We believe that the power consumption of our hybrid computing platform can be further reduced when the sensor module is integrated directly into the analog Kalman filter circuit. Because the measured sensing signal is analog, the analog Kalman filter circuit can process the data directly without using any analog-to-digital conversion, thereby minimizing both the latency and quantization error. In addition, because the memtransistor has one more electrode (gate electrode) than a conventional two-terminal memristor, the device conductance can be adjusted through the gate electrode, which enables simpler circuit configuration. A simple circuit configuration is also expected to contribute in reducing the overall power consumption.

Reviewer #2:
The authors reported a hybrid circuit of sensor fusion for the rotation motion of a drone. Most importantly, they demonstrated the Kalman filter using SnS2 "memristor" based analogue circuits. They also showed this approach has lower latency and is more energy efficient. This is a very good work, however, there are still a few issues that need to be addressed.

Response:
We appreciate the careful and constructive comments from the reviewer. We have provided pointby-point responses to the reviewer's comments below. Our responses are indicated in blue colored text, and the additions (or revisions) to the manuscript are marked in green colored text. independently without using the computing resources of the microcontroller, the computational burden on the microcontroller is reduced, and subsequently a reduction in overall power consumption can be expected. Here, we used transition-metal dichalcogenide (TMD) materials in the channel of the memtransistor, which is a threeterminal hybrid memristor and transistor. The bulk traps located at the tin disulfide (SnS2) nanosheet exhibit a highly reliable nonvolatile resistive switching behavior, which is achievable through the electrical pulse applied to a gate electrode. Finally, we showed that a drone with hybrid computing performs sensor fusion with higher energy efficiency than a drone with only a conventional digital processor.

1) Memristor is a 2-terminal device. The
2) To implement the Kalman filter properly, the resistance of each "memristor" needs to be independent of the voltage on the device. Therefore, a linear I-V curve is preferred. Yes, they included the IV curves at fix gate voltages. But the Kalman filter is worked with Vg=0, and the Vg=0 curve is too hard to see. A

zoom-in view of Vg=0 curve is needed.
Response: Thank you for your comment. As shown in Fig. R6, a linear relationship exists between VD and ID at VG = 0 V. We have added this graph as the inset of Fig S6a.   Figure R6. Measured ID-VD curve at VG = 0 V.

3) If the authors could compare this SnS2 device with other types of memristors and explain why this
device is used, it would be useful.
Response: Thank you for your comment. Because the three-terminal memtransistor can control the device conductance by using the gate electrode, the peripheral circuit configuration for the memtransistor can be simplified more compared to that for the two-terminal memristor. The memristor contains only two electrodes, thereby Vwrite (i.e., voltages for adjusting the conductance) must be applied to both ends of the two electrodes (Fig. R7a). However, these two electrodes are also used for the signal input/output path. Consequently, additional peripheral circuits are required to distribute the input signal (Emea(t)) and Vwrite properly (Fig. R7b).
In contrast, the memtransistor has three electrodes, and the conductance can be adjusted by using the gate electrode independently (Fig. R7c). Therefore, the conductance can be adjusted without additional peripheral circuits (Fig. R7d). In summary, there is no difference between the memristor and the memtransistor in the conductance change characteristic itself, but the memtransistor enables simpler circuit configuration. However, because this study did not verify this advantage experimentally, a brief discussion is added in the CONCLUSION as follows: Revised CONCLUSION: We believe that the power consumption of our hybrid computing platform can be further reduced when the sensor module is integrated directly into the analog Kalman filter circuit. Because the measured sensing signal is analog, the analog Kalman filter circuit can process the data directly without using any analog-to-digital conversion, thereby minimizing both the latency and quantization error. In addition, because the memtransistor has one more electrode (gate electrode) than a conventional two-terminal memristor, the device conductance can be simply adjusted through the gate electrode, which enables simpler circuit configuration. A simple circuit configuration is also expected to contribute to reducing the overall power consumption.

4)
In the paper, the system is built on bread boards. It is a good concept demonstration; however, it is too big/heavy for drone. If they can put the system on a PCB, it would be nice.
Response: Thank you for your comment and we completely agree with it. In this work, for the proof-of-concept of our analog-digital computing platform, we implemented the analog circuit on a bread board.
In fact, to integrate our analog Kalman filter circuit on a PCB and mount it on the drone, 1) a circuit for generating a voltage pulse, 2) a circuit for performing the update-verify feedback process, and 3) a circuit for power distribution are additionally required. Therefore, further research on the design of these peripheral circuits is necessary. We hope that the system-level analog-digital hybrid computing platform will be demonstrated in a near-future study.

REVIEWERS' COMMENTS
Reviewer #1 (Remarks to the Author): Thank you for addressing my comments. I am generally happy with your responses. Nevertheless, while the response is adequate, the change in the manuscript are not sufficiently reflecting these. Some comments while they have been addressed in the response letter, they have not been addressed in the main article. For example Fig. R1 and the related discussions would greatly enhance the paper, either in the main part of the paper or more suitably in the SI. The same goes for Fig, R5. A couple of sentences addressing comment 8 should also be added. A lot of the information discussed in your response to